Bayesian semiparametric modeling for matched case-control studies with multiple disease states.

Samiran Sinha; Bhramar Mukherjee; Malay Ghosh

doi:10.1111/j.0006-341x.2004.00169.x

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Bayesian semiparametric modeling for matched case-control studies with multiple disease states.

Samiran Sinha·Bhramar Mukherjee·Malay Ghosh

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Abstract

We present a Bayesian approach to analyze matched "case-control" data with multiple disease states. The probability of disease development is described by a multinomial logistic regression model. The exposure distribution depends on the disease state and could vary across strata. In such a model, the number of stratum effect parameters grows in direct proportion to the sample size leading to inconsistent MLEs for the parameters of interest even when one uses a retrospective conditional likelihood. We adopt a semiparametric Bayesian framework instead, assuming a Dirichlet process prior with a mixing normal distribution on the distribution of the stratum effects. We also account for possible missingness in the exposure variable in our model. The actual estimation is carried out through a Markov chain Monte Carlo numerical integration scheme. The proposed methodology is illustrated through simulation and an example of a matched study on low birth weight of newborns (Hosmer, D. A. and Lemeshow, S., 2000, Applied Logistic Regression) with two possible disease groups matched with a control group.

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Algorithms Bayes Theorem Biometry Case-Control Studies Female Humans Infant, Low Birth Weight Infant, Newborn Likelihood Functions Logistic Models Markov Chains Monte Carlo Method Pregnancy Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.

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Samiran Sinha, Bhramar Mukherjee, Malay Ghosh. 2004. Bayesian semiparametric modeling for matched case-control studies with multiple disease states.. https://doi.org/10.1111/j.0006-341x.2004.00169.x

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